Biology subjects

Przytycka, T. M.

Publications and source records attributed to Przytycka, T. M..

4 recordsLinked to original sources

bioRxiv2018

Novel analysis of HT-SELEX data elucidates the role of DNA shape in transcription factor binding

Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the question if in the absence of any sequence similarity to the binding motif, DNA shape can still increase probability of binding was yet to be addressed.\n\nTo address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allowed us to detect evidence for the role of DNA shape features in TF binding.\n\nOur approach revealed that, even in the absence of the sequence motif, TFs have propensity to weakly bind to DNA molecules enriched in specific shape features. Surprisingly, we also found that some properties of DNA shape contribute to promiscuous binding of all tested TF families. Strikingly, such promiscuously bound shapes correspond to the most frequent shape formed by the DNA. We propose that this promiscuous binding facilitates diffusing of TFs along the DNA molecule before it is locked in its binding site.

bioinformatics↗

bioRxiv2018

Hidden Markov Models Lead to Higher Resolution Maps of Mutation Signature Activity in Cancer

Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to uncover the characteristic signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome and are biased by factors such as the genomes chromatin structure or replication origins. These factors may lead to statistical dependencies among neighboring mutations, calling for modeling approaches that can account for such dependencies to better estimate mutational process activities.\n\nHere we develop the first sequence-dependent models for mutation signatures. We apply these models to characterize genomic and other factors that influence the activity of previously validated mutation signatures in breast cancer. We find that our tool, SO_SCPLOWIGC_SCPLOWMO_SCPLOWAC_SCPLOW, can accurately assign genomic mutations to mutation signatures, yielding assignments that are of higher likelihood than those obtained with models that assume independence between signatures and align better with current biological knowledge. Our analysis resolves a controversy related to the dependency of APOBEC signatures on replication time and links Signatures 18 and 30 to oxidative damage.\n\nModeling the sequential dependencies of mutation signatures leads to improved estimates of mutation signature activity both at the tumor-level and within specific genomic regions, yielding higher resolution maps of mutation signature activity in cancer.

bioinformatics↗

bioRxiv2017

Detecting Presence Of Mutational Signatures In Cancer With Confidence

Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA mainte-nance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Decomposition of cancers mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from di[ff]erent decomposition methods are not always consistent. Hence, one needs to not only be able to decompose a patients mutational profile into signatures but also to establish the accuracy of such decomposition. We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified very stable and highly unstable signatures, as well as signatures that have been missed altogether. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#signatureestimation.

bioinformatics↗

bioRxiv2017

NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks

Understanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is the ability to infer the transcription factor (TF)-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Due to lack of the biological context specificity, these context-agnostic networks may not provide insight for revealing the precise actions of genes for a specific biological system under concern. Collecting multitude of features required for a reliable construction of GRNs such as physical features (TF binding, chromatin accessibility) and functional features (correlation of expression or chromatin patterns) for every context of interest is costly. Therefore we need methods that is able to utilize the knowledge about a context-agnostic network (or a network constructed in a related context) for construction of a context specific regulatory network.\n\nTo address this challenge we developed a computational approach that utilizes expression data obtained in a specific biological context such as a particular development stage, sex, tissue type and a GRN constructed in a different but related context (alternatively an incomplete or a noisy network for the same context) to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis (NCA) that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, which is non-convex and non-smooth, we provide a general mathematical framework allowing use of the recently proposed Proximal Alternative Linearized Maximization technique and prove that our formulation has the properties required for convergence.\n\nWe tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from 99 hemizygotic lines of the Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method.

bioinformatics↗